Biomarker determination using optical flows

ABSTRACT

A computer-implemented method includes obtaining a video of a subject, the video including a plurality of frames; generating, based on the plurality of frames, a plurality of optical flows; and encoding the plurality of optical flows using an autoencoder to obtain a movement-based biomarker value of the subject.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationSer. No. 62/847,793 filed on May 14, 2019, the contents of which areincorporated here by reference in their entirety.

BACKGROUND

Tracking and predicting movement of subjects by computer vision or byhuman raters has been a difficult problem. Small changes in suchmovement are often too small and irregular to be captured andrecognized. Further, categorization of such movement has been difficult.

SUMMARY

The present disclosure relates to determining biomarkers based on videoof a subject.

In one aspect, the present disclosure describes a method that includes:obtaining a video of a subject, the video including a plurality offrames; generating, based on the plurality of frames, a plurality ofoptical flows; and encoding the plurality of optical flows using anautoencoder to obtain a movement-based biomarker value of the subject.

Implementations of the method may include one or more of the following.In some implementations, the movement-based biomarker value includes afrequency of tremor of the subject. In some implementations, the methodincludes encoding the plurality of optical flows using the autoencoderto obtain a type of tremor of the subject. In some implementations, thetype of tremor includes a hand position of the subject. In someimplementations, the method includes encoding the plurality of opticalflows using the autoencoder to obtain a biomarker type corresponding tothe movement-based biomarker value. In some implementations, thebiomarker type includes a facial muscle group of the subject.

In some implementations, the method includes generating a plurality ofreconstructed optical flows based on an output of the autoencoder, theoutput including the movement-based biomarker value; and training theautoencoder based on a comparison of the plurality of reconstructedoptical flows to the plurality of optical flows. In someimplementations, the method includes generating a plurality ofreconstructed optical flows using an adversarial autoencoder network,the plurality of reconstructed optical flows based on random samplesdrawn from a prior distribution used to train the autoencoder in anadversarial discrimination process, and training the autoencoder usingthe plurality of reconstructed optical flows.

In some implementations, the method includes obtaining a secondplurality of optical flows, the second plurality of optical flows beinglabeled; performing one or more of random translation, random rotation,random scaling, and random cropping on the second plurality of opticalflows, to generate an augmenting plurality of optical flows; andtraining the autoencoder using the augmenting plurality of opticalflows. In some implementations, the method includes training theautoencoder using an adversarial discriminator, including: comparing, bythe adversarial discriminator, an output of the autoencoder, the outputincluding the movement-based biomarker value, to a distribution; andupdating parameters of the autoencoder based on a difference between theoutput of the autoencoder and the distribution.

In some implementations, the method includes training the autoencoderusing labeled data. In some implementations, the labeled data includesexperimentally-derived data, the experimentally-derived data includingdata generated by stimulating a second subject with stimulation having aknown frequency. In some implementations, the labeled data is labeledwith a biomarker type, and training the autoencoder includes trainingthe autoencoder to determine a biomarker value based on implicittraining. In some implementations, the labeled data is labeled with abiomarker value, and training the autoencoder includes training theautoencoder to determine a biomarker type based on implicit training.

In some implementations, generating the plurality of optical flowsincludes: processing the video with one or more of filtering,noise-reduction, or standardization, to generate a plurality ofprocessed video frames; and generating the plurality of optical flowsbased on the plurality of processed video frames. In someimplementations, the method includes generating the plurality of opticalflows based on respective pairs of frames of the plurality of frames. Insome implementations, encoding the plurality of optical flows includes:generating one or more optical flow maps based on the plurality ofoptical flows; and encoding the one or more optical flow maps using theautoencoder to obtain the movement-based biomarker value of the subject.

Particular embodiments of the subject matter described in thisspecification can be implemented to realize one or more of the followingadvantages. In some implementations, movement-based biomarkers may bedetermined more accurately and/or reliably. In some implementations,training data is augmented, such that autoencoder training is improved.In some implementations, optical flows that provide more useful trainingdata for an autoencoder may be generated using an adversarialautoencoder network. In some implementations, an amount of training datarequired for autoencoder training may be decreased. In someimplementations, more useful training data may be obtainedexperimentally.

The details of one or more implementations are set forth in theaccompanying drawings and the description below. Other features andadvantages will be apparent from the description and drawings, and fromthe claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart of exemplary optical flow processing

FIG. 2 is a frame of a video and exemplary optical flow images derivedfrom the video

FIG. 3 is a flow chart of an exemplary optical flow adversarialautoencoder

FIG. 4 is a flow chart of an exemplary optical flow autoencoder and anexemplary adversarial autoencoder generator

FIG. 5 is an exemplary output from optical flow processing

FIG. 6 is a histogram of exemplary prediction accuracies

FIG. 7 is a histogram of exemplary clinical rating scores

FIG. 8 is a flowchart of frequency outputs from an exemplary trainedclassifier to prediction probabilities

FIG. 9 is a line graph of a correlation of a clinical rating of tremorand a prediction probability of tremor from an exemplary model

FIG. 10 is a schematic of an exemplary computer system.

FIG. 11 is a flow chart of an exemplary biomarker prediction process.

FIG. 12 is a flow chart of an exemplary autoencoder training process.

FIG. 13 is a block diagram of an exemplary autoencoder system.

DETAILED DESCRIPTION

The present disclosure relates generally to the field of analyzing andquantifying movement. In a particular example, this disclosure relatesto analyzing movement in a video to obtain biomarkers of a subject. In amore particular example, this disclosure relates to using optical flowanalysis and combined with the use of an autoencoder, in order to obtainbiomarkers of a subject.

Biomarkers are quantifiable characteristics of health. They are used toidentify disease states and assess treatment response. Biomarkers mayinclude visual, auditory, and movement characteristics of a subject.

Tremors are often used as a biomarker in order to diagnose a disease ora condition in a subject. The presence of tremors, or a change in tremorprevalence and/or magnitude over time (for example, a physical amplitudeof a tremor, or a tremor frequency), may be used to diagnose a varietyof conditions, including multiple sclerosis, stroke, and Parkinson'sdisease.

Tremors may be detected by observation, e.g., by a doctor observing asubject during a medical checkup. However, observation-based biomarkerdetection, including the detection of tremors, may be subjective, suchthat the same physical biomarker characteristics are recordeddifferently by different doctors or by the same doctor at differenttimes. In addition, while doctors may be able to perform qualitativeevaluation of biomarkers, quantitative biomarker analysis may requireanalysis aided by video and/or computer technology. For example,determining a frequency of tremor may be difficult or impossible for anunaided doctor.

In some implementations, video analysis may be performed in order toextract movement-based biomarkers. The video analysis may be formulatedas a dimension reduction or feature extraction problem. That is,high-dimensional video is encoded into a feature vector with a smallernumber of components, the components representing the biomarker. Forexample, the components may represent a type of movement and a frequencyof movement.

In accordance with the various embodiments of the present disclosure,improved methods and systems are provided for obtaining movement-basedbiomarkers using a combination of an optical flow analysis and anautoencoder process.

FIG. 1 shows an example of a process flow for extracting movement-basedbiomarkers from a video of a subject. First, a video 100 (for example,an RBG video) of the subject is collected. A video dynamic extractionprocess is then performed on the collected video 100 by a neuralnetwork, such as an optical flow deep neural network (DNN) 102, toextract one or more optical flows 104 from the video 100.

In any of the implementations disclosed herein, a video may representlabeled data (e.g., for use in training and/or autoencoder evaluation)or unlabeled data (e.g., video of a subject under medical examination).

In any of the implementations discussed herein, optical flows mayinclude dynamic characteristics of a video and entirely or substantiallyexclude background and static information of the video. Optical flowsmay emphasize and/or isolate dynamic features of the video. For example,DNN 102 may extract the optical flows 104 based on a comparison ofbrightness and/or colors of pixels and/or shapes across frames of thevideo 100. The analysis performed by the DNN 102 may include, forexample, tracking a movement of a given pixel (as represented by abrightness and/or a color of the pixel) from a first frame to a secondframe.

In some implementations, optical flows are extracted based on analysisof pairs of frames of a video (for example, adjacent frames of the video100).

In some implementations, a video or elements of a video (e.g., frames ofthe video 100) may be processed before optical flows are extracted. Forexample, the video 100 may be processed with one or more of filtering,noise-reduction, or standardization (e.g., aspect-ratio standardizationor resolution standardization to match a standard to which anautoencoder is trained).

In some implementations, a different technique may be used instead of,or in addition to, the DNN 102, in order to extract the optical flows104. For example, a predefined algorithm and/or a machine learningmethod besides a DNN may be used.

FIG. 2 shows a frame 208 of a video of a hand, along with exampleoptical flows 210 extracted from the same video. Each optical flow (forexample, 210 a and 210 b) includes a pixel map showing an intensity ofmovement in each location of the frame. For example, because thebackground features 212 of the frame 208 are static, the backgroundfeatures 212 are not visible in any of the optical flows 210. However, ahand 214 in the frame 208 does move, such that the optical flows 210shows higher intensities corresponding to the hand (with darker colorsindicating a higher intensity of movement).

In some implementations, optical flows may include time-ordered opticalflows. For example, from a first optical flow 210 c to a last opticalflow 210 d, a shape corresponding to the hand starts with low intensity,increases in intensity, and then decreases in intensity. The opticalflows 210 therefore correspond roughly to a single burst of movement ofthe hand 208. For example, if the hand 208 is tremoring, then a full setof optical flows extracted from the entire video might show a pluralityof such bursts of movement, and a frequency of the bursts (as determinedfrom the full set of optical flows) would correspond to a frequency ofthe tremor.

Although the optical flows 210 are shown as images, these images aremerely representative of optical flow data. In any of theimplementations disclosed herein, optical flow images, or other imagesdescribed in this disclosure (e.g., video frames and/or optical flowmaps) may not be directly and explicitly obtained; however, underlyingdata, of which any images would be a representation, may be obtained andused. For example, underlying data may be in the form of an array or atable, and the underlying data may be used for autoencoder training andbiomarker determination as described in this disclosure.

Referring back to the example of FIG. 1, an autoencoder 120 (forexample, a trained deep neural network) encodes the optical flows 104into a feature vector 122 that includes a movement-based biomarker value124 of the subject (e.g., tremor frequency or movement distance). Thisencoding, which leads to a determination of the movement-based biomarkervalue 124, may also be referred to as a “prediction” of themovement-based biomarker value 124.

In any of the implementations disclosed herein, a feature vector mayinclude elements besides a movement-based biomarker value. For example,a feature vector may include a biomarker type. In the example of FIG. 1,the feature vector 122 may include a biomarker type 126. The biomarkertype may indicate a specific categorization of the biomarker. Forexample, in implementations where the video 100 shows a hand tremor, thebiomarker type 126 may indicate a particular arrangement of the hand(e.g., hand held in front of body, or hand held under chin). In someimplementations, the biomarker type 126 may indicate a specific portionof the subject's body (e.g., a specific limb) corresponding to themovement-based biomarker value 124. For example, the biomarker type 126may indicate a particular facial muscle group of the subject's face,such that the feature vector 122 stores information about an action unitobserved in the subject.

In some implementations, the feature vector may include additionalelements. For example, the feature vector 122 may include a plurality ofpairs of elements, each pair of elements including a biomarker value anda corresponding biomarker type. The feature vector 122 may includemultiple biomarker values corresponding to each biomarker type.

In practice, determination of a feature vector based on optical flowscan be a complex process. This is at least because a video (e.g., video100, from which the optical flows 104 are derived) may be recorded inany one of many conditions (e.g., angles of recording and levels of zoomin the recording) and show any one of many arrangements of the subject(e.g., position and arrangement of the body of the subject, and portionof the body of the subject shown), such that the encoding processperformed by the autoencoder is not trivial. Therefore, in any of theimplementations disclosed herein, the autoencoder may include, or betrained using, one or more specific features that enable more accuratedetermination of the feature vector.

At least because optical flows are configured and generated to extractmovement features of the video, an autoencoder-based extraction ofmovement-based biomarker values using optical flows may provide moreaccurate and/or reliable of movement-based biomarkers values than anautoencoder-based method that does not include optical flows.

In some implementations, as disclosed in further detail below, opticalflow maps may be used instead of, or in addition to, optical flows, inorder to train an autoencoder and/or or as inputs to an autoencoderresulting in a determined feature vector.

In any of the implementations disclosed herein, an autoencoder (e.g.,the autoencoder 120) may be trained using labeled data. Values in thefeature vector may be selected in order to improve overall recognitionof future images being analyzed, in some implementations being selectedbased on labels of the labeled data. Because the autoencoder (e.g.,autoencoder 120) and the labeled data are categorized to operate inaccordance with the values in the feature vector (e.g., feature vector122), the autoencoder may be able to more easily recognize featurevectors and determine future feature vectors based on future videoand/or images.

In any of the implementations disclosed herein, an autoencoder (e.g.,autoencoder 330 of FIG. 3) makes use of discrimination and/orreconstruction techniques. When the autoencoder 330 is used to encodeoptical flows 304 extracted from a video 300 using an optical flow DNN302, these techniques may provide better accuracy of the determinedfeature vector 322.

In any of the implementations disclosed herein, an autoencoder may be anadversarial autoencoder based on the use of an adversarialdiscriminator. For example, as shown in FIG. 3, an autoencoder (e.g.,autoencoder 330) is an adversarial autoencoder (e.g., includesadversarial discriminator 332, and/or is trained using adversarialdiscriminator 332). The adversarial discriminator 332 receives, asinputs, an output 336 of the autoencoder 330 (e.g., a type or aquantitative characterization of a biomarker, e.g., a frequency oftremors in an output feature vector) and a function 338 output by aprior distribution generator 334. A difference (error) between adistribution of the output 336 and the function 338 (e.g., adistribution represented by the function 338) may be back-propagatedinto the autoencoder 330 to update parameters of the autoencoder 330, inorder to impose structure on an autoencoder space of the autoencoder(e.g., a latent space of the autoencoder) such that generated featurevectors 322 are mapped to the prior distribution. Therefore, an accuracyof determination of the feature vector 322 may be increased compared toa process not making use of an adversarial autoencoder.

In any of the implementations disclosed herein, an autoencoder mayinclude, and/or may be trained using, a label discriminator. Forexample, as shown in FIG. 3, an autoencoder (e.g., autoencoder 330) istrained using a label discriminator (e.g., label discriminator 340). Thelabel discriminator 340 receives, as input, a data label 342 from alabel element 341 and an output 344 of the autoencoder 330. The datalabel 342 may be, for example, a ground truth label collected duringdata acquisition (e.g., a known, experimentally-determined value of afrequency of tremors, and/or a known, experimentally-determined type ofa motion-based biomarker). A difference (error) between the output 344of the autoencoder 330 and the data label 342 (corresponding to aparticular set of optical flows) may be used to update parameters of theautoencoder 330 via back-propagations (e.g., a gradient descent approachusing chain rules), to train the autoencoder to output correct featurevectors 322.

In any of the implementations disclosed herein, a first element of anoutput feature vector may be used for label discrimination, and asecond, different element of an output feature vector may be used foradversarial discrimination. For example, a biomarker type may be usedfor label discrimination, and a biomarker value may be used foradversarial discrimination. This may improve a resulting accuracy ofdetermined biomarkers.

In any of the implementations disclosed herein, an autoencoder may betrained using a reconstruction network. For example, as shown in FIG. 3,the autoencoder 330 is trained using a reconstruction network, such asreconstruction network 350. The reconstruction network 350 learns togenerate reconstructed optical flows 352 based on feature vectors 322output from the autoencoder 330. For example, for each feature vector322, the reconstruction network 350 may output reconstructed opticalflows 352. The reconstructed optical flows 352 may be compared with theinput optical flows 304, and a difference between the two may beback-propagated into the autoencoder 330 and reconstruction network 350in order to update parameters of the autoencoder 330 and reconstructionnetwork 350. Once the autoencoder 330 and the reconstruction network 350are trained, the feature vector 322 may be an accurate lower-dimensionrepresentation of the input optical flows 304, and the reconstructionnetwork 350 may accurately reconstruct the input optical flows 304.

In some implementations, labeled data (e.g., experimentally-acquireddata) may be insufficient for optimal training of the autoencoder 330,and it may be desirable to generate synthetic data for further training.Therefore, in some implementations, a structured autoencoder may be usedwith the trained reconstruction network in order to synthesize samplesfor training, e.g., using an adversarial autoencoder image generator, asdescribed below in reference to FIG. 4.

In various implementations, an autoencoder (e.g., the autoencoder 330)may include any type of autoencoder, such as a stacked denoisingautoencoder or a variational autoencoder. The autoencoder 330 mayinclude a neural network model (e.g., a DNN model) or another machinelearning model, an output of the neural network model and/or machinelearning model including determined biomarkers.

Some implementations may include further features to enhance an accuracyof biomarker determination.

For example, in any of the implementations disclosed herein, additionaltraining data (e.g., labeled data used as an input to the labeldiscriminator 340 in FIG. 3) may be used to train the autoencoder, theadditional training data having a label similar to, but not the same as,labels of other training data. Weights of the further-trainedautoencoder (e.g., autoencoder 330) may then be used to initialize afinal movement model of the autoencoder 330. This may increase anaccuracy of determined biomarkers by increasing a training range of theautoencoder.

As another example, in any of the implementations disclosed herein,training data used in conjunction with a label discriminator (e.g.,label discriminator 340) may be augmented with additional,artificially-created data. For example, real optical flows (e.g.,optical flows directly extracted from a video), maps of real opticalflows, and/or frames of a video may be randomly processed in order toobtain further optical flows. The random processing may include one ormore of random translation, random rotation, random scaling, and randomcropping, which may increase a variety of training samples. Additionalvideo frames generated by the random processing may be used to generateadditional optical flows. Additional optical flows generated by therandom processing, or based on the additional video frames, may be usedto train the autoencoder, and/or the additional optical flows may beused to generate additional optical flow maps used for autoencodertraining. Additional optical flow maps generated by the randomprocessing, or based on the additional optical flows, may be used forautoencoder training. The use of an augmented training data set (e.g.,for use with a label discriminator 340) may increase an accuracy ofbiomarkers output from the autoencoder, and/or decrease an amount oflabeled training data necessary to train the autoencoder.

Any of the implementations disclosed herein may include an adversarialautoencoder image generator. The adversarial autoencoder image generatormay use labeled random samples drawn from a distribution used for anadversarial training process to generate reconstructed optical flows foruse in autoencoder training. In the example of FIG. 4, real opticalflows 404 are extracted from subject video 400 using an optical flow DNN402. Random samples 470 are drawn from a distribution determined by afunction 472 of a prior distribution generator 474 (e.g., a function anda prior distribution generator used to perform adversarial training tomap an output of the autoencoder to a distribution defined by thefunction). The random samples 470 are combined with corresponding labels476 to generate, by an adversarial autoencoder reconstruction subnetwork478, reconstructed optical flows 480 having the known label 476, theknown label enabling training of the autoencoder 430 using a labeldiscriminator 440, as described above. The label discriminator 440 mayuse the reconstructed optical flows 480 as further training data inorder to update parameters of the autoencoder 430. Therefore, trainingof the autoencoder 430 may be increased, allowing for more accuratedetermination of biomarkers.

In any of the implementations described herein, a function (e.g.,function 472) used to generate reconstructed optical flows using anadversarial autoencoder reconstruction subnetwork may be a function usedin training an autoencoder using an adversarial discriminator. Forexample, in the implementation of FIG. 3, the function 338 may be usedfor training using an adversarial discriminator and also used forgenerating reconstructed optical flows using an adversarial autoencodersubnetwork, as described in reference to FIG. 4. In some implementation,different functions may be used for these processes.

In any of the implementations described herein, an adversarialautoencoder image generator may be trained using an adversarialdiscrimination process, as described in reference to FIG. 3. In someimplementations, an adversarial discrimination process may be used toimpose structure on an autoencoder space of an autoencoder and also usedto train an adversarial autoencoder image generator. In someimplementations, an image generator different from the adversarialautoencoder image generator, trained using adversarial discrimination,may be used.

In any of the implementations described herein, a reconstruction network(e.g., reconstruction network 350) may include an adversarialautoencoder reconstruction subnetwork, and the reconstruction networkmay perform the reconstructed optical flow generation described inreference to the adversarial autoencoder reconstruction subnetwork 478.

The implementation of FIG. 4 also includes a label discriminator 440, asdescribed in reference to FIG. 3. The adversarial autoencoder imagegeneration shown in FIG. 4 may be combined with any or all of theelements described in reference to FIGS. 1 and 3. Features described inreference to any implementation of FIGS. 1, 3, and 4 may be combinedwith or used in conjunction with features of another implementation ofFIGS. 1, 3, and 4. In addition, the processes shown in any of FIGS. 1,3, and 4 may be modified to include other features described in thisdisclosure.

In some implementations, in order to encode video into feature vectorsdescribing biomarker values and/or types, movement labels are used inorder to force biomarker type and/or biomarker value separation in modeltraining. Such implementations may include one of labeling movement by abiomarker value (e.g., tremor frequency), labeling movement by abiomarker type (e.g., a hand position), or labeling movement by both abiomarker value and a biomarker type.

In some implementations, collected data is labeled with a biomarkervalue but not a biomarker type. However, an autoencoder trained on thiscollected data may predict not only biomarker value but also biomarkertype (a latent variable). In some implementations, the autoencoderassumes that, in the training data (e.g., a population of images and/orvideo used to the train the autoencoder), movement can be decomposedinto a biomarker value and a biomarker type, and that movement can becompletely represented by the biomarker value and the biomarker type.That is, although the autoencoder may train on only the biomarker value,a remaining element besides the biomarker value out of two elements inan output feature vector implicitly represents biomarker type.Therefore, once trained, the autoencoder may predict, for example, notonly movement frequency but also movement type, even though movementtype labels may not be available as a ground truth in the training data(e.g., experimental data).

In some implementations, collected data is labeled with a biomarker typebut not a biomarker value. In such implementations, implicit trainingfor the latent variable can be performed (as described above for thebiomarker type), such that the trained autoencoder may predict, forexample, not only movement type but also movement frequency, even thoughmovement frequency labels may not be available as a ground truth in thetraining data.

In some implementations, collected data is labeled with a biomarker typeand with a biomarker value. The autoencoder may be trained using bothlabels, resulting, in some implementations, in a stronger separationbetween biomarker type and biomarker value due to the supervisedlearning process with more complete information incorporated.

In some implementations, an autoencoder may determine three or morevalues and be trained using data labeled with fewer values than thethree or more values.

As described above, encoding of movement using optical flows has manypractical applications. In clinical and other medical research areas,determination of movement biomarkers based on subject video may bevaluable for predicting and/or recognizing movement for the purposes ofconfirming medication adherence, and for detecting any suspicious,undesirable, or unexpected movement of an individual during a medicationadministration process. Encoding using optical flows may be used todetermine relative motion of the hand or other body part of the subjectwhen the subject is performing one or more predetermined motions,exercises, tasks, or other expected movements. Such motions, exercises,or tasks may be performed as part of another action, or specifically inresponse to a request to the subject to perform a specific action. Sucha request may be presented to the subject on the display of a mobiledevice or may be part of an action typically performed by theindividual, either with prompting or as performed by the individual in anormal course of activity. For example, an application on a mobile phonemay prompt the subject to perform an action, and a camera of the mobilephone may subsequently record a video of the action. One or moreprocessors located on the mobile phone and/or at a remote server maythen perform the processes disclosed herein.

Processes disclosed herein may be applied to the monitoring of tremors.In such an implementation, a feature vector may include a movementfrequency that can be directly used for clinical diagnosis. Thefrequency of movement can be correlated to actual tremor, and the actualtremor in turn correlated to diagnosis, monitoring, and monitoring ofprogression of disease. The frequency of movement may be used toevaluate the condition of a subject in a vegetative state.

In some implementations, the processes disclosed herein may be appliedto action unit and expression determination. In such an implementation,action units may be based on facial muscle groups, and a feature vectormay include 2 or more elements. A first element may be a biomarker typeto represent different muscle groups. A second element may be abiomarker value giving a measure of movement frequency. In someimplementations, a third element of the feature vector may be abiomarker value representative of movement intensity. In someimplementations, the feature vector may include an expression label.

A movement framework according to an action unit and expressiondetermination implementation may be used to predict action units insteadof, or in addition to, using a landmark-based action unit identificationmethod (e.g., OpenFace). Implementations as described herein may allowfor a higher level of analysis and/or further allow for more direct andprecise monitoring of changes in facial action units, which in turn maybe more indicative of expression changes or changes in other attributesof a subject.

Implementations for determining action units may include action unitlabeling. However, in some implementations, manual labeling of actionunits or muscle groups, such as when labeling units in the face of avideo subject, may be labor-intensive and subject to observer errors andinconsistency.

Therefore, implementations employing features disclosed herein (e.g.,features described in reference to FIGS. 1, 3, and/or 4) may also beused in conjunction with labeled action unit label data collected usingan electric stimulator. Electrodes may be placed on subject faces atlocations of different muscle groups, and the frequency and intensity ofstimulation delivered to the electrodes may be controlled in order toinduce different facial movements while recording videos. The frequencyand intensity of the facial movements correspond to the frequency andintensity of stimulation. Therefore, a known “ground truth” for thefacial movements may be generated, the known ground truth labels beingused for training the autoencoder (e.g., using a label discriminator asdescribed above). Data labeled in this manner may be more useful (e.g.,for training an autoencoder) than data labeled by raters or by othermethods.

An electrode stimulation process may be employed with other muscles inthe body, e.g., by placing electrodes on the hands of a subject.

Some implementations using optical flows to derive biomarkers may beapplied to medication adherence determination. A video may record asubject taking medication, and biomarkers may be extracted from opticalflows of the video (using an autoencoder as described above) in order todetermine biomarkers including movement type and movement frequency. Theautoencoder may determine whether the subject successfully or trulyadministered the medicine.

In some implementations, medication adherence determination videos, orother videos, may be used for disease-specific processes. For example,videos of subject having known diseases may be used for training (with adisease-indicating label), and a feature vector may include a biomarkertype indicating a disease of the subject. Videos may be clustered bydifferent patient diseases to build a prediction model for anautoencoder. In some implementations, therefore, the autoencoder maypredict (determine) a disease of a subject.

In order to provide further details and examples of optical flows usedin conjunction with an autoencoder to determine biomarkers, anexperimental example is now disclosed.

A model was first trained on collected volunteer data, and was thenfurther evaluated employing patient videos (data collected fromindividuals using a medication adherence monitoring system). The patientvideos were collected at a variety of focus levels. Collected patientvideos were first scored by raters from 0 to 4 based on a distance offinger movement in the videos. However, due, in some implementations, tolack of means to estimate distance from videos, such rater-based tremorscoring may be subject to intra- and inter-observation variability.

The volunteer data was labeled with accurate movement frequency labels.Tremors were produced by using an electronic pulse massager to deliverelectrical stimulus to one of each volunteer's hands via two 2×2 inchelectrodes. The pulse massager allowed for applying controlled stimuliat regular intervals to the hand to recreate the amplitude and frequencyof a clinical tremor. One electrode was placed on each side of the hand,as localized as possible to the abductor pollicis brevis and between thefirst and second dorsal interrossei muscles. The median nerve branch ofthe brachial plexus, which controls coarse movements of the hand, wastargeted for stimulus to recreate the appearance of tremor. Thefrequency and amplitude of the applied stimuli were used as thefrequency and amplitude labels for training an autoencoder.

42 videos from 23 volunteers were recorded with a hand forward pose atthree different stimulus frequencies each, the stimulus frequenciesbeing 0 Hz (no stimulation), 4 Hz, 10 Hz, using the volunteer dataacquisition protocol, as described above. These frequencies were chosenbased on commonly-observed clinical tremor frequencies. Each videolasted approximately 15 seconds and was divided into multipleoverlapping video clips at 0.5 second intervals, each clip having alength of 2 seconds. In total, 6696 video clips were prepared in thismanner. The length of video clips (2 seconds) was determined to coversufficient hand movements for tremor quantification. Because of thediscrepancy in subject responses to electronic stimuli, the videos werefirst manually reviewed, and the videos in which no induced tremors wereobserved were excluded. Then, optical flow maps derived from frames ofthe remaining videos were down-sampled to 64×64 pixels each in width andheight to lower computational cost.

Although this experimental example uses stimulated hand tremors, datalabeled based on direct stimulation or on direct measurement (e.g.,using sensing electrodes applied to the body of a volunteer) may be usedin combination with any of the implementations disclosed herein. Labeleddata obtained in this manner (as opposed to, e.g., data labeled by arater) may enhance an accuracy of a trained autoencoder by providingmore accurate and reliable training data.

In any of the implementations disclosed herein, optical flows may beprocessed into optical flow maps that include information derived fromtwo or more optical flows. For example, an optical flow map may includemovement data extracted from a video at multiple time-points or acrossmultiple pairs of frames. At least because optical flow maps may includedata of multiple individual optical flows, optical flow maps may be usedin place of, or in addition to, optical flows with respect to any of thefeatures disclosed herein. For example, an autoencoder may be trained tooutput feature vectors based on one or more input optical flow maps. Asanother example, an autoencoder may be trained using labeled opticalflow maps in conjunction with a label discriminator. As another example,a reconstruction network or an adversarial autoencoder image generatormay output reconstructed optical flow maps for use in training anautoencoder (e.g., with a discriminator, e.g., a label discriminator).Because underlying data may be represented in either optical flow formor optical flow map form, either or both forms may be used in any of theimplementations described herein.

FIG. 5 shows examples of data, including example optical flows andoptical flow maps, resulting from the volunteer studies described above.The first row 560 shows single optical flows (e.g., optical flow 561) ofthe subjects for each applied stimulus frequency. These individualoptical flows do not show patterns of movement.

In any of the implementations disclosed herein, optical flow maps mayinclude a representation of optical flow data that has been reduced indimensionality. For example, optical flow maps may be generated using acutting and/or an averaging across one dimension of a multi-dimensionaldataset of multiple optical flows, in order to reduce an amount of dataincluded in the optical flow maps, e.g., in order to make the opticalflow maps more easily understood, and/or in order to decrease an amountof optical flow map data that must be processed (e.g., by anautoencoder).

For example, in the example of FIG. 5, the second and third rows 562,564 each show, for each of the three applied stimulus frequencies, twoexample optical flow maps (e.g., optical flow maps 566 a and 566 b), theexample optical flow maps showing an intensity of movement along aspatial cut of each optical flow extracted from a given video (for row562, ay-cut; for row 564, an x-cut). A three-dimensional x-y-t datasethas been cut in order to produce the two-dimensional optical flow mapsof rows 562, 564.

A horizontal axis of each optical flow map represents a spatial positionalong the respective cut (an x value for row 562 and ay value for row564), and a vertical axis of each optical flow map represents a time. Acolor of each pixel of the example optical flow maps indicates anintensity of movement at the given x-t coordinate or y-t coordinate, asdetermined in a corresponding optical flow.

Optical flows in the “0 Hz” column (corresponding to videos where nostimulus was applied) show no particular patterns. However, opticalflows in the “4 Hz” and “10 Hz” columns (corresponding to videos inwhich those stimulus frequencies were applied) show stripe patterns(indicated by dark gray arrows, e.g., arrow 568) indicative of tremor.The cyclical appearance of these patterns in the t direction indicates afrequency of the tremor, while the localized appearance of thesepatterns in the x- or y-direction indicates that the tremor is localizedin a particular portion of each video frame. Because optical flow map566 b was extracted from a video in which the stimulation frequency washigher than for optical flow map 566 a, the stripes in optical flow map566 b have a higher frequency of cycle in the t-direction than thestripes in optical flow map 566 a.

After optical flow extraction of the dynamic information from thevolunteer tremor videos, a three-way deep neural network classifierautoencoder learned to determine tremor frequency (determine whethermovement had a frequency of 0 Hz, 4 Hz, or 10 Hz) based on the extractedoptical flows. The autoencoder was then supplemented with three furtherfeatures.

Although this example uses three stimulation frequencies of 0 Hz (nostimulation applied), 4 Hz, and 10 Hz, in other implementations otherstimulation frequencies and/or more stimulation frequencies may beapplied. In some implementations, experimentally-obtained data mayinclude data labeled with many stimulation frequencies (or anotherbiomarker value), and an autoencoder may be trained to determine afrequency value over a continuous range rather than, or in addition to,classifying between three discrete, pre-selected values.

As a first supplement, using the same image acquisition system asdescribed above, another volunteer (who was not among the 23 volunteersfrom whom the validation data was acquired), was also video recordedemploying the hand forward pose at three other frequencies: 0.99 Hz,3.98 Hz, and 9.96 Hz, each video lasting 30 seconds. The autoencoder wastrained based on this dataset, and the trained weights of theautoencoder were used to initialize a final movement model for trainingusing data of the 23 volunteers, as described above in reference to FIG.3.

As a second supplement, original optical flow maps were processed withrandom translation, rotation, scaling, and cropping to increase thevariety of training samples. These random processes may simulatereal-world variety in video recording conditions. Optical flow mapsresulting from this processing were added to the training data toaugment the training set at every other iteration, as described above inreference to FIG. 3. In some implementations, random processing toaugment a training set may be performed every iteration, or at anotherrate different from every other iteration.

As a third supplement, the autoencoder was trained using an adversarialautoencoder image generator, as described above in reference to FIG. 4.A same number of random samples as had been used for the originaltraining samples were drawn from a prior distribution after theadversarial autoencoder network was optimized. These random samples werepassed to an adversarial autoencoder subnetwork as seed latent vector.Reconstructed optical flows and/or optical flow maps were generated bythe adversarial autoencoder subnetwork were pooled together with theexperimentally-collected samples and the augmenting randomly-generatedoptical flows of the second supplement in order to increase the totaltraining sample for autoencoder training.

Due at least to the limited number of subjects available for dataacquisition, leave-one-out cross-validation method was used to evaluatetrained autoencoders. Eight testing subjects were selected from theoriginal 23 subjects for validation. These testing subjects were basedupon a determination of a sufficient length of induced tremors recordedin their videos. Eight different models were trained (with four versionseach, as described below), corresponding to the eight testing subjects.First, an adversarial autoencoder was trained excluding data from alleight testing subjects; then, an individual autoencoder (classifier)subnetwork was trained for each testing subject.

FIG. 6 depicts example evaluations of prediction accuracy (i.e.,determination of biomarkers) for the eight testing subjects using fourdifferent methods: a basic three-way classifier to choose between 0 Hz,4 Hz, and 10 Hz for a determined movement frequency (legend “Basic” inFIG. 6), the basic three-way classifier having the features shown inFIG. 3; the basic three-way classifier supplemented with weightinitialization (legend “+Weight Initialize” in FIG. 6); the basicthree-way classifier supplemented with weight initialization and imageaugmentation (legend “+Image Augment” in FIG. 6); and the basicthree-way classifier supplemented with weight initialization, imageaugmentation, and adversarial autoencoder image generation (legend “+AAEGenerator” in FIG. 6). The right-most bar group in FIG. 6 is the averageaccuracy of all the testing subjects. The accuracy of FIG. 6 is derivedfrom a comparison of movement frequency as determined by an autoencoder(i.e., a biomarker value and/or biomarker type of a feature vectoroutput by an autoencoder) as compared to the experimentally knownmovement frequencies imposed by the stimuli.

As is shown in FIG. 6, model weight initialization with a similar dataset may stabilize an autoencoder optimization process and therebyincreased biomarker determination accuracy. Image augmentation may alsoincrease determination accuracy by adding variety to training data.Adversarial autoencoder image generation may also boost predictionaccuracy, in some implementations by compensating for a relatively smallamount of data used for autoencoder training.

As a further evaluation of movement-based biomarker determination basedon optical flow analysis, an autoencoder was trained by data from 33clinical videos collected from nine essential tremor patients. Each ofthe clinical videos was rated by a rater, with scoring from 0 to 4determined by a distance of tremor movement. TABLE 1 shows a specificset of labeling criteria.

Video segments showing left or right hand forward poses were extractedfor movement model evaluation. FIG. 7 depicts the histogram of theclinical scores (scores determined by raters) for the extracted videosegments.

TABLE 1 Hand 0 1 1.5 2 2.5 3 3.5 4 Score R-forward none barely visible<1 cm 1-<3 cm 3-<5 cm 5-<10 cm 10-20 cm >20 cm 2 L-forward none barelyvisible <1 cm 1-<3 cm 3-<5 cm 5-<10 cm 10-20 cm >20 cm 1.5

The extracted video segments with left or right hand forward poses werealso divided into multiple overlapping video clips at 0.5 secondintervals, each clip having a length of 2 seconds. The original clinicalvideo segments were recorded at six different sites with differentdevices and resolutions. To eliminate this device difference, theoriginal video segments were cropped to match the size of the volunteervideos described above, and the original video segments were alsodown-sampled to the same 64×64 resolution as the volunteer videos.

Tremor in the clinical videos was rated by movement distance incentimeters, as described above. However, in some cases, it may bedifficult for even a trained expert to estimate an absolute movementdistance from videos in a consistent way among different observations,or for different raters to agree on a rating for a given observation.Taking into consideration this intra- and inter-rater variability andthe lack of ground truth, the evaluation of clinical data did not focuson absolute accuracy. Instead, the evaluation targeted correlationbetween the clinical rating scores and the determined biomarkers of anautoencoder model.

While the output of the three-way classifier from the trainedautoencoder model is a categorical movement frequency (0 Hz, 4 Hz, and10 Hz), clinical scores indicate tremor severity. Therefore, as shown inFIG. 8, instead of directly using categorical frequency outputs from thetrained autoencoder, continuous values from a last layer of a DNN of theautoencoder were used as the prediction probability of tremor in videos,using the equation P_(tremor)=1−c₂−c₃.

FIG. 9 shows mean prediction probability of tremor plotted againstclinical rating score of tremor severity for the clinical videos. Errorbars in the plot are 95% confidence intervals of the mean, using at-distribution. The prediction probability of tremor from theautoencoder is highly correlated with the clinical rating score oftremor severity (Pearson's correlation coefficient r=0.99). Theseresults may indicate that the autoencoder is more confident indetermining tremor-related biomarkers when a human rater indicates moresevere tremor, whereas the model is less confident when a human raterindicates milder tremor (e.g., less visually-noticeable tremor).

As shown in FIG. 9, autoencoder determinations of “no tremor” are lessaccurate than “tremor” determinations. One reason for this difference inperformance (in this particular example) may that that training data wascollected from healthy volunteers who were able to hold their hands verysteady, almost motionless in many recorded videos, whereas the patientsin the clinical videos may have had difficulty holding their handssteady even without an underlying tremor condition, due to their ages orunderlying health conditions. That is, even in clinical videos withtremor rating 0, unintentional hand motion may be observed more oftenthan is observed in healthy volunteers.

The validation results shown in FIG. 9 demonstrate that a movement-basedbiomarker model using optical flows, trained by labeled volunteer data(e.g., volunteer data obtained experimentally as described above), can,in some implementations, be used as an instrument for consistent,reproducible, and objective tremor quantification. In someimplementations, validation results may differ from those shown in FIG.9, which are drawn from a single, merely exemplary study.

In some implementations, methods and features described above may beimplemented by one or more computing devices. As shown in FIG. 10, acomputing device 1000 may include at least one or more of: one or moreprocessors 1010, one or more memories 1020, one or more communicationsinterfaces 1030, one or more storage devices 1040, and one or moreinterconnecting buses 1050.

As shown in FIG. 11, implementations as described herein may performoperations including: obtaining a video of a subject 1100, obtainingframes of the video 1110, generating a plurality of optical flows 1120,and predicting a movement-based biomarker of the subject 1130. In someimplementations, the predicting is based on the plurality of opticalflows. In some implementations, the predicting is based on one or moreoptical flow maps generated (1140) based on the plurality of opticalflows. In some implementations, the video is pre-processed (1150). FIG.1 shows an example of a system and process according to FIG. 11. Dashedlines in FIG. 11 indicate steps flows that may be optional in someoptical flow generation processes (and/or movement-based biomarkerdetermination processes) based on a video of a subject.

As shown in FIG. 12, implementations as described herein may performoperations including: obtaining a labeled training video 1200, obtainingframes of the video 1210, generating a plurality of optical flows 1220,and training an autoencoder using a discriminator (1230), theautoencoder being trained to determine biomarkers. In someimplementations, the labeled training video is preprocessed (1240). Insome implementations, random processing is performed on the frames, togenerate additional frames 1250 (on which the plurality of optical may,in some implementations, be based). In some implementations, one or moreoptical flow maps are generated (1260) based on the generated pluralityof optical flows and/or based on additional optical flows generated byperforming random processing on the generated plurality of optical flows(1270). In some implementations, random processing is performed on theone or more optical flow maps, to generate one or more additionaloptical flow maps (1280). In some implementations, the autoencoder maybe trained based on the optical flow maps (e.g., using a discriminator).Dashed lines in FIG. 12 indicate step flows that may be optional in sometraining implementations based on a labeled training video.

As described above and in reference to FIG. 13, in some implementations,other training methods and/or additional training methods (including,for example, pre-processing of training video data) may be used.

FIGS. 3 and 4 each show examples of systems and processes according toFIGS. 11 and 12. FIG. 11 shows an example process including “predict amovement-based biomarker,” and FIG. 12 shows an example processincluding “train an autoencoder using a discriminator.” FIGS. 3 and 4each incorporate aspects of both of these processes. Features ofimplementations according to FIGS. 1, 3, 4, 11, 12, and 13 may becombined, even if the features are not explicitly shown in a singleimplementation.

All or a portion of the features described above (e.g., discriminators,encoders, and networks) may be implemented as computational modules. Forexample, in one example, as shown in FIG. 13, modules may include one ormore of: an autoencoder module 1300 that determines one or morebiomarkers (e.g., biomarker values and/or biomarker types) based oninput data; a label discrimination module 1310 that receives output fromthe autoencoder and performs discrimination training of the autoencoderbased on differences between the output from the autoencoder and labeleddata (e.g., experimentally-obtained labeled data, labeled data generatedby random processing, and/or reconstructed data based on an autoencoderoutput and a reconstruction network module 1320 operating, for examplewithin or as part of an adversarial autoencoder generator); thereconstruction network module 1320, which may return reconstructedoptical flows and/or reconstructed optical flow maps to the autoencodermodule for training; an adversarial discrimination module 1330 thattrains the autoencoder, e.g., based on a comparison of an autoencoderoutput to a distribution; a video processing module 1340 that performsoperations (e.g., standardizing, filtering, and frame extraction) oninput data; an optical flow processing module 1350, which may extract orprocess optical flows and/or optical flow maps in accordance with theimplementations disclosed herein, and provide input to the autoencoder1300; and a labeled data processing module 1360, which may providelabeled data to the label discriminator module 1310, receive data fromthe reconstruction network 1320 for distribution, and/or generateaugmented data (e.g., randomly-processed optical flows and/or opticalflow maps). Details on the operations of each of these modules are givenelsewhere in this disclosure.

Implementations as described herein may not include each module shown inFIG. 13. For example, in some implementations, an optical flowprocessing module may receive video data directly, without a videoprocessing module. As another example, in some implementations, one ormore of the label discrimination module, the adversarial discriminationmodule, and the reconstruction network module may be removed, and such asystem may still be operable to determine movement-based biomarkers asdisclosed herein. Implementations are not limited to these examples. Inaddition, although connection lines in the example of FIG. 13 aredepicted as solid, some described connection between modules may not beincluded in every implementation (e.g., are optional), and additionalconnections not depicted in FIG. 13 may be included in someimplementations, in accordance with the implementations describedherein.

Modules may be implemented as individual software programs, combinedwith other modules in software, and/or implemented fully or partially(in some implementations, combined with other modules) as discretephysical components

Therefore, in accordance with the various embodiments of the disclosure,improved methods and systems are provided for determining movement-basedbiomarkers based on optical flow analysis by an autoencoder.

All or part of the processes described herein and their variousmodifications (hereinafter referred to as “the processes”) can beimplemented, at least in part, via a computer program product, i.e., acomputer program tangibly embodied in one or more tangible, physicalhardware storage devices that are computer and/or machine-readablestorage devices for execution by, or to control the operation of, dataprocessing apparatus, e.g., a programmable processor, a computer, ormultiple computers. A computer program can be written in any form ofprogramming language, including compiled or interpreted languages, andit can be deployed in any form, including as a stand-alone program or asa module, component, subroutine, or other unit suitable for use in acomputing environment. A computer program can be deployed to be executedon one computer or on multiple computers at one site or distributedacross multiple sites and interconnected by a network.

Actions associated with implementing the processes can be performed byone or more programmable processors executing one or more computerprograms to perform the functions of the calibration process. All orpart of the processes can be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) and/or an ASIC(application-specific integrated circuit). Other embedded systems may beemployed, such as NVidia® Jetson series or the like.

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read-only storagearea or a random access storage area or both. Elements of a computer(including a server) include one or more processors for executinginstructions and one or more storage area devices for storinginstructions and data. Generally, a computer will also include, or beoperatively coupled to receive data from, or transfer data to, or both,one or more machine-readable storage media, such as mass storage devicesfor storing data, e.g., magnetic, magneto-optical disks, or opticaldisks. Processors “configured” to perform one or more of the processes,algorithms, functions, and/or steps disclosed herein include one or moregeneral or special purpose processors as described herein as well as oneor more computer and/or machine-readable storage devices on whichcomputer programs for performing the processes are stored.

Tangible, physical hardware storage devices that are suitable forembodying computer program instructions and data include all forms ofnon-volatile storage, including by way of example, semiconductor storagearea devices, e.g., EPROM, EEPROM, and flash storage area devices;magnetic disks, e.g., internal hard disks or removable disks;magneto-optical disks; and CD-ROM and DVD-ROM disks and volatilecomputer memory, e.g., RAM such as static and dynamic RAM, as well aserasable memory, e.g., flash memory.

Components may be coupled (e.g., communicably coupled) over one or morenetworks or physically within a device. Coupling may include thecapability to transmit data, including instructions, back and forthbetween the components.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a computerhaving a display device, e.g., a CRT (cathode ray tube) or LCD (liquidcrystal display) monitor, for displaying information to the user and akeyboard and a pointing device, e.g., a mouse or a trackball, by whichthe user can provide input to the computer. Other kinds of devices canbe used to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, ortactile input. In addition, a computer can interact with a user bysending documents to and receiving documents from a device that is usedby the user; for example, by sending web pages to a web browser on auser's device in response to requests received from the web browser.

Embodiments of the subject matter described in this specification can beimplemented in a computing system that includes a back end component,e.g., as a data server, or that includes a middleware component, e.g.,an application server, or that includes a front end component, e.g., aclient computer having a graphical user interface or a web browserthrough which a user can interact with an implementation of the subjectmatter described in this specification, or any combination of one ormore such back end, middleware, or front end components. The componentsof the system can be interconnected by any form or medium of digitaldata communication, e.g., a communication network. Examples ofcommunication networks include a local area network (LAN) and a widearea network (WAN), e.g., the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. In someembodiments, a server transmits data, e.g., an HTML page, to a userdevice, e.g., for purposes of displaying data to and receiving userinput from a user interacting with the user device, which acts as aclient. Data generated at the user device, e.g., a result of the userinteraction, can be received from the user device at the server.

Other implementations not specifically described herein are also withinthe scope of the following claims. Logic flows depicted in the figuresdo not require the particular order shown, or sequential order, toachieve desirable results. In addition, other actions may be provided,or actions may be eliminated, from the described flows, and othercomponents may be added to, or removed from, the described systems.Likewise, actions depicted in the figures may be performed by differententities or consolidated. Furthermore, various separate elements may becombined into one or more individual elements to perform the functionsdescribed herein. In some cases, multitasking and parallel processingmay be advantageous.

While visual signals are mainly described in this invention, other datacollection techniques may be employed, such as thermal cues or otherwavelength analysis of the face or other portions of the body of theuser. These alternative data collection techniques may, for example,reveal other movement-based biomarkers of the patient, such as changesin blood flow, etc. Additionally, visual depth signal measurements,combined with the use of optical flows, may allow for capture subtlefacial surface movement correlated with the symptom that may bedifficult to detect with typical color images.

It should be noted that any of the above-noted inventions may beprovided in combination or individually. Elements of differentembodiments described herein may be combined to form other embodimentsnot specifically set forth above. Elements may be left out of theprocesses, computer programs, etc. described herein without adverselyaffecting their operation. Furthermore, the system may be employed inmobile devices, computing devices, cloud based storage and processing.Camera images may be acquired by an associated camera, or an independentcamera situated at a remote location. Processing may be similarly beprovided locally on a mobile device, or a remotely at a cloud-basedlocation, or other remote location. Additionally, such processing andstorage locations may be situated at a similar location, or at remotelocations.

Moreover, the separation of various system modules and components in theembodiments described above should not be understood as requiring suchseparation in all embodiments, and it should be understood that thedescribed program components and systems can generally be integratedtogether in a single software product or packaged into multiple softwareproducts.

What is claimed is:
 1. A computer-implemented method comprising:obtaining a video of a subject, the video comprising a plurality offrames; generating, based on both a subject portion and a backgroundportion of each frame of the plurality of frames, a plurality of opticalflows representing movement in the plurality of frames; and providingthe plurality of optical flows as inputs to an autoencoder to obtain, asan output of the autoencoder, a representation having a reduceddimensionality compared to the plurality of optical flows, therepresentation including a movement-based biomarker value of thesubject, wherein the autoencoder is trained based on comparisons betweentraining optical flows and reconstructed optical flows generated basedon autoencoder processing of the training optical flows.
 2. Thecomputer-implemented method of claim 1, wherein the movement-basedbiomarker value comprises a frequency of tremor of the subject.
 3. Thecomputer-implemented method of claim 2, wherein the representationcomprises a type of tremor of the subject.
 4. The computer-implementedmethod of claim 3, wherein the type of tremor comprises a hand positionof the subject.
 5. The computer-implemented method of claim 1, whereinthe representation comprises a biomarker type corresponding to themovement-based biomarker value.
 6. The computer-implemented method ofclaim 5, wherein the biomarker type comprises a facial muscle group ofthe subject, and wherein the movement-based biomarker value comprises ameasure of intensity of movement of the facial muscle group or a measureof frequency of movement of the facial muscle group.
 7. Thecomputer-implemented method of claim 1, further comprising: generating aplurality of reconstructed optical flows using an adversarialautoencoder network, the plurality of reconstructed optical flows basedon random samples drawn from a prior distribution used to train theautoencoder in an adversarial discrimination process, and training theautoencoder using the plurality of reconstructed optical flows.
 8. Thecomputer-implemented method of claim 1, further comprising: obtaining asecond plurality of optical flows, the second plurality of optical flowsbeing labeled; performing one or more of random translation, randomrotation, or random scaling on the second plurality of optical flows, togenerate an augmenting plurality of optical flows; and training theautoencoder using the augmenting plurality of optical flows.
 9. Thecomputer-implemented method of claim 1, further comprising training theautoencoder using an adversarial discriminator, comprising: comparing,by the adversarial discriminator, the output of the autoencoder to adistribution; and updating parameters of the autoencoder based on adifference between the output of the autoencoder and the distribution.10. The computer-implemented method of claim 1, comprising training theautoencoder using labeled data.
 11. The computer-implemented method ofclaim 10, wherein the labeled data comprises experimentally-deriveddata, the experimentally-derived data comprising data generated bystimulating a second subject with stimulation having a known frequency.12. The computer-implemented method of claim 10, wherein the labeleddata is labeled with a biomarker type, and wherein training theautoencoder comprises training the autoencoder to determine a biomarkervalue based on implicit training.
 13. The computer-implemented method ofclaim 10, wherein the labeled data is labeled with a biomarker value,and wherein training the autoencoder comprises training the autoencoderto determine a biomarker type based on implicit training.
 14. Thecomputer-implemented method of claim 1, wherein generating the pluralityof optical flows comprises: processing the video with one or more offiltering, noise-reduction, or standardization, to generate a pluralityof processed video frames; and generating the plurality of optical flowsbased on the plurality of processed video frames.
 15. Thecomputer-implemented method of claim 1, comprising generating theplurality of optical flows based on respective pairs of frames of theplurality of frames.
 16. The computer-implemented method of claim 1,wherein providing the plurality of optical flows as inputs to theautoencoder comprises providing one or more optical flow maps as inputsto the autoencoder, the optical flow maps generated based on theplurality of optical flows.